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The extracellular concentration of H2O2 in surface aquatic environments is controlled by a balance between photochemical production and the microbial synthesis of catalase and peroxidase enzymes to remove H2O2 from solution. In any kind of incubation experiment, the formation rates and equilibrium concentrations of reactive oxygen species (ROSs) such as H2O2 may be sensitive to both the experiment design, particularly to the regulation of incident light, and the abundance of different microbial groups, as both cellular H2O2 production and catalase–peroxidase enzyme production rates differ between species. Whilst there are extensive measurements of photochemical H2O2 formation rates and the distribution of H2O2 in the marine environment, it is poorly constrained how different microbial groups affect extracellular H2O2 concentrations, how comparable extracellular H2O2 concentrations within large-scale incubation experiments are to those observed in the surface-mixed layer, and to what extent a mismatch with environmentally relevant concentrations of ROS in incubations could influence biological processes differently to what would be observed in nature. Here we show that both experiment design and bacterial abundance consistently exert control on extracellular H2O2 concentrations across a range of incubation experiments in diverse marine environments. During four large-scale (>1000 L) mesocosm experiments (in Gran Canaria, the Mediterranean, Patagonia and Svalbard) most experimental factors appeared to exert only minor, or no, direct effect on H2O2 concentrations. For example, in three of four experiments where pH was manipulated to 0.4–0.5 below ambient pH, no significant change was evident in extracellular H2O2 concentrations relative to controls. An influence was sometimes inferred from zooplankton density, but not consistently between different incubation experiments, and no change in H2O2 was evident in controlled experiments using different densities of the copepod Calanus finmarchicus grazing on the diatom Skeletonema costatum (<1 % change in [H2O2] comparing copepod densities from 1 to 10 L−1). Instead, the changes in H2O2 concentration contrasting high- and low-zooplankton incubations appeared to arise from the resulting changes in bacterial activity. The correlation between bacterial abundance and extracellular H2O2 was stronger in some incubations than others (R2 range 0.09 to 0.55), yet high bacterial densities were consistently associated with low H2O2. Nonetheless, the main control on H2O2 concentrations during incubation experiments relative to those in ambient, unenclosed waters was the regulation of incident light. In an open (lidless) mesocosm experiment in Gran Canaria, H2O2 was persistently elevated (2–6-fold) above ambient concentrations; whereas using closed high-density polyethylene mesocosms in Crete, Svalbard and Patagonia H2O2 within incubations was always reduced (median 10 %–90 %) relative to ambient waters.

Glider vehicles are now perhaps some of the most prolific providers of real-time and near-real-time operational oceanographic data. However, the data from these vehicles can and should be considered to have a long-term legacy value capable of playing a critical role in understanding and separating inter-annual, inter-decadal, and longterm global change. To achieve this, we have to go further than simply assuming the manufacturer’s calibrations, and field correct glider data in a more traditional way, for example, by careful comparison to water bottle calibrated lowered CTD datasets and/or “gold” standard recent climatologies. In this manuscript, we bring into the 21st century a historical technique that has been used manually by oceanographers for many years/decades for field correction/inter-calibration, thermal lag correction, and adjustment for biological fouling. The technique has now been made semi-automatic for machine processing of oceanographic glider data, although its future and indeed its origins have far wider scope. The subject of this manuscript is drawn from the original Description of Work (DoW) for a key task in the recently completed JERICO-NEXT (Joint European Research Infrastructure network for Coastal Observatories) EU-funded program, but goes on to consider future application and the suitability for integration with machine learning. Refereed 14.A Sea surface salinity Subsurface salinity TRL 8 Actual system completed and "mission qualified" through test and demonstration in an operational environment (ground or space) Manual (incl. handbook, guide, cookbook etc) Standard Operating Procedure 2019-12-03